Multi-Sensor Data Collection, Fusion, and Analytics to Support Effective Performance Measures in Construction Projects

多传感器数据收集、融合和分析支持建筑项目中的有效绩效衡量

• 批准号: RGPIN-2018-06760
• 负责人: Zhu, Zhenhua
• 金额: $ 1.88万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=669909
• 关键词: Multi; Sensor; Data; Collection; Fusion

Although there is still a debate on whether construction productivity is declining or not, one note of agreement is that the room for improvement is significant. Consider an often-repeated phrase that “You can't improve what you don't measure.” Therefore, providing the effective measures of construction productivity becomes essential and has been identified as one of five opportunities for breakthrough improvements in construction efficiency and competitiveness in the next 2 to 10 years. The objective of this research is to investigate the feasibility of integrating multi-sensory data from video, acoustic, thermal, and other Internet of Things (IoT) sensors to facilitate the measures of construction operation productivity on a construction site. The proposed research work includes three main steps. It starts with the design of a portable multi-sensor hardware platform that could be easily deployed to monitor onsite construction operations. Then, the data fusion and analysis are conducted to retrieve construction operation information from the monitoring data. The retrieved information is further explored to discover and model real construction operation processes on the site. This way, the productivity of the construction operations in the processes could be measured and analyzed in a systematic way with less time, cost, and efforts required. The research is expected to help contractors and/or subcontractors evaluate the efficiency of their work more often (e.g. on an hourly or daily basis), so that the productivity problems on active construction projects could be detected and corrected quickly. The effectiveness of the research work will be evaluated in real construction projects in Quebec, Canada.

尽管对于建筑生产力是否在下降仍有争论，但有一点是一致的，即改进的余地很大。考虑一个经常重复的短语，“你不能改善你不衡量的东西。”因此，提供有效的建筑生产力措施变得至关重要，并已被确定为在未来2至10年内突破性提高建筑效率和竞争力的五个机会之一。本研究的目的是调查整合来自视频，声学，热学和其他物联网（IoT）传感器的多传感器数据的可行性，以促进施工现场施工作业生产率的测量。建议的研究工作包括三个主要步骤。它从设计一个便携式多传感器硬件平台开始，该平台可以轻松部署以监控现场施工操作。然后，进行数据融合和分析，从监测数据中提取施工操作信息。检索到的信息被进一步探索，以发现和模拟现场的真实的施工操作过程。通过这种方式，可以以系统的方式测量和分析过程中施工操作的生产率，所需的时间，成本和工作量更少。预计该研究将帮助承包商和/或分包商更频繁地评估其工作效率（例如，每小时或每天），以便能够快速检测和纠正正在进行的建筑项目的生产力问题。研究工作的有效性将在加拿大魁北克的真实的建筑项目中进行评估。